I'm currently have a longitudinal dataset with dependent variable Y measures over two time points (variable = Time) across 4 groups (variable = Group). This variable Y is a continuous variable describing brain structural properties. The range for this variable can differ based on what property is being analyzed (from between around 2000-9000 or from between around 1-10). I was planning to run separate models for each variable Y.
I want to take account the variability of each subjects (variable = Subject) intercept when looking at a linear fit across the two time points. I want to do this across all the models with different Y variables.
I am looking to use a mixed model approach to determine the prediction of Y while controlling for two continuous covariates, which are age and another continuous structural brain property which is usually in the 5 digit range (variables B + C).
Currently I am using the lme4 package and the lmer() function as below
lmer(Y ~ Group*Time + B + C + (1 + Time | Subject), data = data)
Is the correct model to accurately model the differences in groups over time while taking to account subject variability?
Thanks in advance!